Face Recognition-Based Door Lock Security System Using TensorFlow Lite

Abstract

A door security system utilizing face recognition technology based on TensorFlow Lite has been developed to enhance access security and convenience. This research aims to design a system capable of accurately recognizing faces in real time, integrating it with door lock devices, and ensuring user data security. Employing the waterfall method, the system was implemented using an ESP32-CAM microcontroller and deep learning algorithms. Testing results demonstrated a face recognition accuracy of 91% in identifying and processing commands from 200 trials with ten facial variations. Successful integration with door lock devices was achieved through serial communication. The system also features activity log recording for monitoring purposes. This solution offers greater practicality and security than RFID systems as it eliminates the need for physical cards. This research contributes to developing more sophisticated and user-friendly home security systems, with the potential for further enhancements in recognition capabilities under various lighting conditions and integration with other biometric technologies